Exploiting knowledge graph and text for ranking entity types

Abstract

Most of the online queries target entities and the type of an entity is a key piece of information. Entity type helps us to understand what an entity is and how it relates to other entities mentioned in a document. Search engine result pages (SERPs) often surface facts and entity type information from a background Knowledge Graph (KG) in response to queries that carry a semantic information need. In a KG, an entity usually holds multiple type properties. For example, popular types attached to the entity `Donald Trump' via rdfs:type statements might be Person, Businessman, and Leader. However, other types of this entity, e.g., Solicitor, Restaurateur, and Writer might also be interesting to some users. Unpopular entity types can be useful for tail queries like, for example, `Is Donald Trump an American Television Producer' or `Is Donald Trump an American Television Actor'. It is then important to, given an entity in a KG, rank entity types attached to the entity by relevance to a certain user and information need as not always the most popular type is the most informative within a textual context. In this paper we address the entity type ranking problem by means of KG embedding models. In our work, we show that entity type ranking can be seen as a special case of the KG completion problem. Embeddings can be learned from the structural, probabilistic and contextual description information of the entities. We propose and evaluate our methods to find the most relevant entity type based on collection statistics and on the graph structure interconnecting entities and types. Experimental results show that our proposed approaches outperform the state-of-the-art type ranking models while, at the same time, being more efficient and scalable. Our approach focuses on the task of ranking a set of types associated to an entity in a background knowledge graph to select the most relevant types.